Atmospheric and Oceanic Physics
The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming
Bosong Zhang and Timothy M. Merlis
Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's
climate have recently been developed. However, the ability of these ML models to generalize beyond the training distribution remains an open question. In this study, we evaluate the
climate response of several state-of-the-art ML models (ACE2-ERA5, NeuralGCM, and cBottle) to a uniform sea surface temperature warming, a widely used
benchmark for evaluating
climate change. We assess each ML model's performance relative to a physics-based general circulation model (NOAA's Geophysical Fluid Dynamics Laboratory AM4) across key diagnostics, including surface air temperature, precipitation, temperature and wind profiles, and top-of-atmosphere radiation. While the ML models reproduce key aspects of the physical model response, particularly the response of precipitation, some exhibit notable departures from robust physical responses, including radiative responses and land region warming. Our results highlight the promise and current limitations of ML models for
climate change applications and suggest that further improvements are needed for robust out-of-sample generalization.
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History and Philosophy of Physics · Cross-listed from quant-ph
Comment on Marek Czachor article entitled "On Relativity of Quantumness as Implied by Relativity of Arithmetic and Probability"
Krzysztof Sienicki and Mikołaj Sienicki
Czachor's model of hierarchical arithmetics begins with a valid formal premise but fixes the key probability mapping g by importing the Born rule and Fubini-Study metric from standard
quantum mechanics, where Born probabilities are Kolmogorov within a fixed measurement context. This g is then applied in a non-Newtonian hidden-variable setting, producing a hybrid framework whose agreement with
quantum correlations is built in by design, not derived from new physics, and thus does not constitute a genuine counterexample to Bell's
theorem
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Fluid Dynamics · Cross-listed from gr-qc
Penetrating the horizon of a hydrodynamic white hole
Nisha Jangid and Arnab K. Ray
In a shallow-water radial
outflow the horizon of a hydrodynamic white hole coincides with a standing circular hydraulic jump. The jump, caused by viscosity, makes the horizon visible as a circular front, standing as a barrier against the entry of waves within its circumference. The blocking of waves causes a pile-up at the horizon of the white hole, for which surface tension is mainly responsible. Conversely, it is also because of surface tension that the waves can penetrate the barrier. The penetrating waves (analogue Hawking quanta) tunnel through the barrier with a decaying amplitude, but a large-amplitude instability about the horizon is possible.
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Plasma Physics · Cross-listed from astro-ph.HE
Relativistic Jets and Winds in Radio-Identified Supermassive Black Hole Binary Candidates
Andrew G. Sullivan, Roger D. Blandford, Anna Synani et al. (+4 more)
Supermassive black hole binary systems (SMBHBs) are thought to emit the recently discovered nHz gravitational wave background; however, not a single individual nHz source has been confirmed to date. Long-term
radio-monitoring at the Owens Valley
Radio Observatory has revealed two potential SMBHB candidates: blazars PKS 2131-021 and PKS J0805-0111. These sources show periodic flux density variations across the electromagnetic spectrum, signaling the presence of a good clock. To explain the emission, we propose a generalizable jet model, where a mildly relativistic wind creates an outward-moving helical channel, along which the ultra-relativistic jet propagates. The observed flux variation from the jet is mostly due to aberration. The emission at lower frequency arises at larger radius and its variation is consequently delayed, as observed. Our model reproduces the main observable features of both sources and can be applied to other sources as they are discovered. We make predictions for
radio polarization, direct imaging, and emission line variation, which can be tested with forthcoming observations. Our results motivate future numerical simulations of jetted SMBHB systems and have implications for the fueling, structure, and
evolution of blazar jets.
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All authors Andrew G. Sullivan, Roger D. Blandford, Anna Synani, Philipe V. de la Parra, Noémie Globus, Mitchell C. Begelman, Anthony C.S. Readhead
Chemical Physics
Theoretical Exploration of the Diene-Transmissive Hetero-Diels-Alder Strategy Toward Boron-Functionalized Octahydroquinolines
Amine Rafik, Abdeljabbar Jaddi, Khalid Abbiche et al. (+3 more)
A diene-transmissive hetero-Diels-Alder strategy, grounded in previous experimental works and employing boronated dienophiles, is proposed for the synthesis of boron-bearing octahydroquinolines. To assess its feasibility, three representative reactions were investigated, and their thermodynamics were evaluated in toluene and acetonitrile at various temperatures using the WB97X-D level of theory. The peri-, regio-, stereo-, and $\pi$-facial selectivities were predicted. The reactions mechanisms were elucidated through exploration of the reaction pathways. The predictions are consistent with available experimental work, and show the reactions are feasible with low to moderate polarity. The results also demonstrate that the reactions selectivity can in some cases be tuned by judicious choice of reaction conditions to deliver specific products with high selectivity.
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All authors Amine Rafik, Abdeljabbar Jaddi, Khalid Abbiche, Mohammed Salah, Miguel Carvajal, Khadija Marakchi
Instrumentation and Detectors
Gas Electroluminescence in a Dual Phase Xenon-Doped Argon Detector
James W. Kingston, Jianyang Qi, Jingke Xu et al. (+7 more)
Noble element detectors using argon or xenon as the detection medium are widely used in the searches for rare
neutrino and dark matter interactions. Xenon doping in
liquid argon can preserve attractive properties of an argon target while enhancing the detectable signals with properties of xenon. In this work, we deployed a dual-phase
liquid argon detector with up to 4% xenon doping in the
liquid and studied its gas electroluminescence properties as a function of xenon concentration. At $\sim$2% xenon doping in
liquid argon, we measured $\sim$34 ppm of xenon in the gas and observed $\sim$2.5 times larger electroluminescence signals in the detector than those in pure argon. By analyzing signals recorded by photosensors of different wavelength sensitivities, we confirm that the argon gas electroluminescence process is strongly affected by the addition of xenon. We propose an analytical model to describe the underlying energy transfer mechanism in argon-xenon gas mixtures. Lastly, the implications of this measurement for low energy ionization signal detection will be discussed.
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All authors James W. Kingston, Jianyang Qi, Jingke Xu, Ethan P. Bernard, Adam D. Tidball, Alec W. Peck, Nathaniel S. Bowden, Mani Tripathi, Kaixuan Ni, Shawn Westerdale
Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP
Mapping the Cloud-Driven Atmospheric Dynamics & Chemistry of an Isolated Exoplanet Analog with Harmonic Signatures
Michael K. Plummer, Francis P. Cocchini, Peter A. Kearns et al. (+12 more)
Young planetary-mass objects and brown
dwarfs near the L/T spectral transition exhibit enhanced spectrophotometric variability over field brown
dwarfs. Patchy clouds, auroral processes, stratospheric hot spots, and complex carbon chemistry have all been proposed as potential sources of this variability. Using time-resolved, low-to-mid-resolution spectroscopy collected with the JWST/NIRISS and NIRSpec instruments, we apply harmonic analysis to SIMP J013656.5+093347, a highly variable, young, isolated planetary-mass object. Odd harmonics (k = 3) at pressure levels ~ 1 bar, corresponding to iron and forsterite cloud formation, suggest a potential North-South hemispheric asymmetry in the cloudy, and likely equatorial, regions. We use the inferred harmonics, along with 1-D substellar atmospheric models, to map the flux variability by atmospheric pressure level. We identify distinct time-varying structures in the near-infrared that we interpret as planetary-scale wave (e.g., Rossby or Kelvin)-associated cloud modulation. We detect deviations from bulk (composite) variability in water (S/N = 14.0), carbon monoxide (S/N = 13.0), and methane (S/N = 14.9) molecular signatures. Forsterite cloud modulation is anti-correlated with overlying carbon monoxide and water abundances and correlated with deep methane absorption, suggesting complex interaction between cloud formation, atmospheric chemistry, and temperature structure. Furthermore, we identify distinct harmonic behavior between methane and carbon monoxide absorption bands, providing evidence for time-resolved disequilibrium carbon chemistry. At the lowest pressures (< 100 mbar), we find mapped methane lines transition from absorption to emission, supporting evidence of high-altitude auroral heating via electron precipitation.
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All authors Michael K. Plummer, Francis P. Cocchini, Peter A. Kearns, Allison McCarthy, Étienne Artigau, Nicolas B. Cowan, Roman Akhmetshyn, Johanna Vos, Evert Nasedkin, Channon Visscher, Björn Benneke, René Doyon, Stanimir A. Metchev, Jason F. Rowe, Genaro Suárez
Chemical Physics · Cross-listed from cs.LG
Transformers Discover Molecular Structure Without Graph Priors
Tobias Kreiman, Yutong Bai, Fadi Atieh et al. (+3 more)
Computational simulations play a central role in scientific discovery, and machine learning (ML) has emerged as a promising alternative to traditional physics-based modeling. However, scientific modeling requires physically meaningful predictions, raising a fundamental question for data-driven methods: to what extent can physical inductive biases-that is, prior assumptions about the structure of the physical world-emerge by learning from data alone? In this work, we study atomistic modeling, a representative field in the computational sciences where ML architectures have historically embedded strong physical inductive biases-such as
geometric locality and graph structure. We develop a systematic understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions. We find that the model autonomously recovers key physical structure, such as learned interatomic interaction strengths that mirror classical electrostatics and interaction cutoffs consistent with traditional physical models. We further demonstrate predictable neural scaling law behavior with increased data and compute, and find accuracy on certain metrics competitive with physics-informed architectures. Our results clarify the boundary between engineered inductive biases and learnable physical structure, suggesting that general-purpose architectures can serve as principled baselines for scientific modeling by learning fundamental physical structure directly from data.
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All authors Tobias Kreiman, Yutong Bai, Fadi Atieh, Elizabeth Weaver, Eric Qu, Aditi S. Krishnapriyan
Instrumentation and Detectors · Cross-listed from hep-ex
Predicting the single-site and multi-site event discrimination power of dual-phase time projection chambers
A.B.M. Rafi Sazzad, Clarke A. Hardy, Xiang Dai et al. (+8 more)
Dual-phase xenon time projection chambers (TPCs) are widely used in searches for rare dark matter and
neutrino interactions, in part because of their excellent position reconstruction capability in 3D. Despite their millimeter-scale resolution along the charge drift axis, xenon TPCs face challenges in resolving single-site (SS) and multi-site (MS) interactions in the transverse plane. In this paper, we build a generic TPC model with an idealized signal readout, and use Fisher Information (FI) to predict its theoretical capability of differentiating SS and MS events using the electroluminescence signal. We also demonstrate via simulation that, when only statistical
photon noise is present, the theoretical limits can be approached with conventional reconstruction
algorithms like maximum likelihood estimation, and with a convolutional neural network classifier. The implications of this study on future TPC experiments will be discussed.
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All authors A.B.M. Rafi Sazzad, Clarke A. Hardy, Xiang Dai, Jingke Xu, Brian G. Lenardo, Felicia Sutanto, Nicholas A. Antipa, Jeremy D. Koertzen, Prince John, Abraham Akinin, Teal J. Pershing
Fluid Dynamics
Bouncing to coalescence transition for droplet impact onto moving liquid pools
Daniel M. Harris, Luke F.L. Alventosa, Oliver Sand et al. (+5 more)
A droplet impacting a deep fluid bath is as common as rain over the ocean. If the impact is sufficiently gentle, the mediating air layer remains intact, and the droplet may rebound completely from the interface. In this work, we experimentally investigate the role of translational bath motion on the bouncing to coalescence transition. Over a range of parameters, we find that the relative bath motion systematically decreases the normal Weber number required to transition from bouncing to merging. Direct numerical simulations demonstrate that the depression created during impact combined with the translational motion of the bath enhances the air layer drainage on the upstream side of the droplet, ultimately favoring coalescence. A simple
geometric argument is presented that rationalizes the collapse of the experimental threshold data, extending what is known for the case of axisymmetric normal impacts to the more general 3D scenario of interest herein.
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All authors Daniel M. Harris, Luke F.L. Alventosa, Oliver Sand, Eli Silver, Arman Mohammadi, Thomas C. Sykes, Alfonso A. Castrejon-Pita, Radu Cimpeanu
Data Analysis and Statistics · Cross-listed from hep-ex
Wasserstein normalized autoencoder for anomaly detection
CMS Collaboration
A novel anomaly detection
algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution–a Boltzmann distribution where the energy is the reconstruction error of the autoencoder–and the distribution of the training data. This
algorithm has been developed and applied to the identification of semivisible jets–conical sprays of visible standard model particles and invisible dark matter states–with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated standard model processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard autoencoder fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.
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Chemical Physics
Microscopic Structure of Aqueous Alkylamine mixtures: a Computer Simulation Study
Martina Požar, Lena Friedrich, Bernarda Lovrinčević et al. (+3 more)
Aqueous alkylamine mixtures are studied by computer simulations in order to understand the microscopic origin of the water rich side prominent x-ray scattering pre-peaks reported in a recent study. These pre-peaks are puzzling in view of the apparently contradicting facts that neat amines show pre-peaks much weaker than neat alkanols, while water-rich aqueous alcohols do not. These observations can be intuitively rationalized by noting that the amine head group have two hydrogen atoms when the hydroxyl group have only one, but they oppose the following two facts: i) computer simulations show micro-heterogeneity for both systems; ii) amines mix with water better than alcohols, both over larger concentrations and alkyl tails lengths. The study of the atom-atom pair correlation functions and related structure factors allows to understand the microscopic molecular details. The most interesting observation is that the amine head groups accumulate preferentially at the surface of the water domains, and increasingly better with longer alkyl tail, thus allowing to stabilize both the water and alkylamine domains, hence avoiding macroscopic demixing, except at high water concentrations when amines are scarce to achieve efficient surface saturation. The amine domains appear as disordered
bilayers. Hence, aqueous amines are analogous to an inverse micelle melt and as precursor micro-emulsion. This stable micro-segregation produces large domain oscillations in the long range part of the correlation functions, translating into positive pre-peaks and negative anti-peaks in the related structure factors, the latter which contribute destructively to produce the prominent scattering pre-peak observed in the x-ray experiments. The model dependence is shown to be quite important, both for water and solute models. The CHARMM-AA model associated with the SPC/E model seems to be a good compromise.
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All authors Martina Požar, Lena Friedrich, Bernarda Lovrinčević, Michael Paulus, Christian Sternemann, Aurélien Perera
Chemical Physics
Imaging and controlling electron motion and chemical structural dynamics of biological system in real time and space
Ligong Zhao, Mohamed Sennary, Dina Hussein et al. (+4 more)
Ultrafast electron microscopy (UEM) has found widespread applications in physics, chemistry, and materials science, enabling real-space imaging of dynamics on ultrafast timescales. Recent advances have pushed the temporal resolution of UEM into the attosecond regime, enabling the attomicroscopy technique to directly visualize electron motion. In this work, we extend the capabilities of this powerful imaging tool to investigate ultrafast electron dynamics in a biological system by imaging and controlling light induced electronic and chemical changes in the conductive network of multicellular cable bacteria. Using electron energy loss spectroscopy (EELS), we first observed a
laser induced increase in {\pi}-electron density, accompanied by spectral peak broadening and a blueshift features indicative of enhanced conductivity and structural modification. We also traced the effect of ultrafast
laser pumping on bulk plasmon electron oscillations by monitoring changes in the plasmon like resonance peak. Additionally, we visualized
laser induced chemical structural changes in cable bacteria in real space. The imaging results revealed carbon enrichment alongside a depletion of nitrogen and oxygen, highlighting the controllability of chemical dynamics. Moreover, time resolved EELS measurements further revealed a picosecond scale decay and recovery of both {\pi}-electron and plasmonic features, attributed to electron
phonon coupling. In addition to shedding light on the mechanism of electron motion in cable bacteria, these findings demonstrate ultrafast modulation and switching of conductivity, underscoring their potential as bio-optoelectronic components operating on ultrafast timescales.
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All authors Ligong Zhao, Mohamed Sennary, Dina Hussein, Anaisa Coelho, Tingting Yang, Mohamed Y. El-Naggar, Mohammed Th. Hassan
Optics
Fiber-to-chip grating couplers for Lithium Niobate on Sapphire
Xiang Chen, Jia-Qi Wang, Yuan-Hao Yang et al. (+6 more)
Lithium Niobate on Sapphire (LNOS) is an emerging platform for photonic integrated
circuits, offering unique properties such as a wide transparency window, high nonlinearity, and strong electro-optic, nonlinear, and acousto-optic effects. Efficient light coupling between optical fibers and on-chip waveguides is crucial for practical applications. We present the design, simulation, and experimental demonstration of high-efficiency fiber-to-chip grating couplers for LNOS. The grating coupler design employs a self-imaging approach with a fixed period and linearly diminished filling factor, enabling a negative diffracted angle to match the fiber array and suppress higher-order diffraction. Numerical simulations predict a coupling efficiency of 42% at 1550 nm wavelength. The grating couplers are fabricated on an X-cut, 400 nm thick LN film with a 220 nm etching depth using electron beam lithography and inductively coupled
plasma etching. Experimental characterization using a fiber array and a 6-axis displacement stage reveals a single-end coupling efficiency exceeding 20%, confirming the effectiveness of the design. The demonstrated grating couplers pave the way for efficient light coupling in LN-on-Sapphire photonic
circuits, enabling diverse applications in classical and
quantum information processing, sensing, and nonlinear optics.
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All authors Xiang Chen, Jia-Qi Wang, Yuan-Hao Yang, Zheng-Xu Zhu, Xin-Biao Xu, Ming Li, Xi-Feng Ren, Guang-Can Guo, Chang-Ling Zou
Plasma Physics
A neural network approach to kinetic Mie polarimetry for particle size diagnostics in nanodusty plasmas
Alexander Schmitz, Andreas Petersen and Franko Greiner
The analysis of the size of nanoparticles is an essential task in
plasma technology and dusty
plasmas. Light scattering techniques, based on Mie theory, can be used as a non-invasive and in-situ diagnostic tool for this purpose. However, the standard back-calculation methods require expertise from the user. To address this, we introduce a neural network that performs the same task. We discuss how we set up and trained the network to analyze the size of
plasma-grown amorphous carbon nanoparticles (a:C-H) with a refractive index n in the range of real(n) = 1.4-2.2 and imag(n) = 0.04i-0.1i and a radius of up to several hundred nanometers, depending on the used wavelength. The diagnostic approach is kinetic, which means that the particles need to change in size due to growth or etching. An uncertainty analysis as well as a test with experimental data are presented. Our neural network achieves results that agree with those of prior fitting
algorithms while offering higher methodical stability. The model also holds a major advantage in terms of computing speed and automation.
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Biological Physics · Cross-listed from cs.LG
Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference
Jens Behrmann, Maria R. Cervera, Antoine Wehenkel et al. (+8 more)
Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond these metrics, PPG waveforms contain richer physiological information, as recent deep learning (DL) studies demonstrate. However, DL models often rely on features with unclear physiological meaning, creating a tension between predictive power, clinical interpretability, and sensor design. We address this gap by introducing PPGen, a biophysical model that relates PPG signals to interpretable physiological and optical parameters. Building on PPGen, we propose hybrid amortized inference (HAI), enabling fast, robust, and scalable estimation of relevant physiological parameters from PPG signals while correcting for model misspecification. In extensive in-silico experiments, we show that HAI can accurately infer physiological parameters under diverse noise and sensor conditions. Our results illustrate a path toward PPG models that retain the fidelity needed for DL-based features while supporting clinical interpretation and informed hardware design.
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All authors Jens Behrmann, Maria R. Cervera, Antoine Wehenkel, Andrew C. Miller, Albert Cerussi, Pranay Jain, Vivek Venugopal, Shijie Yan, Guillermo Sapiro, Luca Pegolotti, Jörn-Henrik Jacobsen
Accelerator Physics
Accurate Modelling of Intrabeam Scattering and its Impact on Photoinjectors for Free-Electron Lasers
Thomas Geoffrey Lucas, Paolo Craievich, Eduard Prat et al. (+2 more)
Intrabeam scattering (IBS) is a fundamental effect that can limit the performance of high-brightness electron machines, yet it has so far been neglected in standard modelling of RF photoinjectors. Recent measurements at SwissFEL reveal that the slice energy spread (SES) in the injector is significantly underestimated by conventional tracking codes. In this work, we present a dedicated Monte Carlo simulation model that accurately predicts the IBS-induced SES growth in the photoinjector of an X-ray free-electron
laser. The simulations are benchmarked against SES measurements at the SwissFEL as well as theoretically supported by a new analytical model. The results demonstrate that IBS-induced SES growth occurs throughout the injector, most prominently in the electron source, and must be taken into account when assessing photoinjector performance. We further show that while the 5D brightness is largely conserved, the 6D brightness undergoes notable degradation with propagation, underscoring the need to include IBS in the accurate design and optimization of photoinjectors.
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All authors Thomas Geoffrey Lucas, Paolo Craievich, Eduard Prat, Sven Reiche, Erion Gjonaj
Computational Physics · Cross-listed from quant-ph
Improving neural network performance for solving quantum sign structure
Xiaowei Ou, Tianshu Huang and Vidvuds Ozolins
Neural
quantum states have emerged as a widely used approach to the numerical study of the ground states of non-stoquastic
Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pre-trained phase network. We introduce a modified
stochastic reconfiguration method that effectively uses differing imaginary time steps to evolve the amplitude and phase. Using a larger time step for phase optimization, this method enables a simultaneous and efficient training of phase and amplitude neural networks. The efficacy of our method is demonstrated on the Heisenberg J_1-J_2 model.
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Optics
Hyperbolic Continuous Topological Transition in Real Space
Junke Liao, Tao Hou and Huanyang Chen
Hyperbolic topological transitions refer to the transformation of is isofrequency contours in
hyperbolic materials from one
topology (e.g.,
hyperbolic) to another (e.g., elliptical or a different
hyperbolic topology). However, current research remains limited to investigating topological transitions in momentum space, thereby hindering the simultaneous real-space observation of distinct
hyperbolic states and their associated topological transitions within a single system. In this work, we investigate real-space
hyperbolic continuous topological transitions using gradient-index (GRIN) lenses, exemplified by
hyperbolic Luneburg lens. By introducing Wick rotations, we demonstrate how spatially modulated refractive indices, mediated by variations in out-of-plane permittivity, drive continuous transitions between
hyperbolic Type I and Type II
topologies. Furthermore, using a harmonic oscillator model, we uncover the intrinsic relationship between the parameter E of
hyperbolic Luneburg lens and its predominant topological behavior, whether
hyperbolic Type I or Type II, and extend this concept to a broader framework of Morse lenses. This work provides a theoretical foundation for designing materials with tunable topological properties, advancing applications in photonics, metamaterials, and beyond.
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Fluid Dynamics · Cross-listed from astro-ph.SR
Chemical transport by weakly nonlinear internal gravity waves in stars
Yifeng Mao and Daniel Lecoanet
While internal
gravity waves (IGWs) are known to transport chemicals in the radiative zones of
stars, substantial uncertainty remains regarding the magnitude and physical mechanisms of this transport. Most previous studies have relied on heuristic theories or numerical simulations that are difficult to extrapolate to
stellar parameters. In this work, we present the first rigorous asymptotic calculation of (passive) chemical transport by IGWs, in the limit of small wave amplitude. The net vertical transport induced by a coherent packet of waves is found to be proportional to the horizontal mean of $(w^S+w^E)^2$, where $w^S$ and $w^E$ are the vertical Stokes and Eulerian mean velocities. The transport scales as the fourth power of wave amplitude and quadratically with time, and we verify these scalings with numerical simulations. In the weakly damped regime, the dominant transport coefficients scale as $\omega^{-2}\kappa^2$, where $\omega$ is the wave frequency and $\kappa$ is the radiative diffusivity. Because the transport can be positive or negative depending on the wave phases, the effective mixing induced by the superposition of coherent wave packets is expected to scale with the eighth power of wave amplitude. These results provide a theoretical foundation for making accurate predictions of IGW-induced chemical transport in
stellar evolution models.
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Accelerator Physics
An electro-optical bunch profile monitor for FCC-ee
M. Reissig, E. Bründermann, S. Funkner et al. (+11 more)
The Future Circular
Lepton Collider (FCC-ee) presents challenges for a longitudinal bunch profile monitor due to its wide range of bunch lengths and charge densities across its four distinct operational modes. For commissioning, monitoring the top-up injection, and energy calibration, the FCC-ee requires non-destructive, single-shot measurements of the bunch length and profile. This contribution proposes an in-
vacuum electro-optical (EO) longitudinal bunch profile monitor for single-shot measurements at high repetition rates, building on the successful EO monitor at the Karlsruhe Research Accelerator (KARA) at the Karlsruhe Institute of Technology. A novel single-pass conceptual design for the in-
vacuum holder of the electro-optical
crystal is presented, utilizing prisms instead of a mirror to guide the
laser through the
crystal, which additionally allows measurements of the long bunches foreseen for FCC-ee operation mode at the Z-pole energy. A first prototype has been constructed and tested at the in-air test stand of the CERN Linear Electron Accelerator for Research (CLEAR). Results from the prototype tests are presented, demonstrating the proof of principle for the single-pass prism-based EO monitor design for FCC-ee.
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All authors M. Reissig, E. Bründermann, S. Funkner, L. L. Grimm, B. Härer, G. Niehues, J. L. Steinmann, A.-S. Müller R. Corsini, A. Gilardi, S. Mazzoni, C. Pakuza, A. Schloegelhofer, T. Lefevre, P. Korysko
Optics
Erdos-Turan photonic Ising machines with record-high coupling resolution
Huaqiang Li, Guangfeng Wang, Erez Hasman et al. (+2 more)
Ising machines have emerged as promising platforms for efficiently tackling a wide range of combinatorial optimization problems relevant to resource allocation, statistical inference and deep learning, yet their practical utility is fundamentally constrained by the coarse resolution of
spin-
spin couplings (Jij). Current implementations, relying on direct modulation of physical parameters, achieve at most 256 discrete coupling levels, which severely hinder the faithfully modeling of arbitrary real-valued interactions in realistic applications. Here we present a novel photonic Ising machine that encodes
spins in random
lattices while programming couplings in the momentum space of light. By introducing the Sidon set-a mathematical structure ensuring pairwise difference uniqueness - and employing the Erdos-Turan bound, we establish an optical framework in which each
spin pair can be assigned a unique Jij. This approach decouples the resolution limit from hardware modulation to the spatial precision in the momentum space of light. Experimentally, we demonstrate a record-high coupling resolution of 7,038 on a simple photonic platform, surpassing previous Ising machines. Our results highlight the power of uniting discrete mathematics with momentum-space photonics, paving the way toward scalable Ising machines capable of faithfully modeling real-world optimization problems.
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All authors Huaqiang Li, Guangfeng Wang, Erez Hasman, Bo Wang, Xianfeng Chen
Instrumentation and Detectors
The Application of VMM3A Readout for Multi-Grid Neutron Detectors
A.Backis, F.Piscitelli, D.Pfeiffer et al. (+6 more)
The T-REX
neutron spectrometer at the European Spallation Source will use Multi-Grid Technology, which is a voxelised proportional counter relying on\mathrm{^{10}B_{4}C} coatings to detect the scattered
neutrons. Measurements of the position dependence of pulse-height and relative detection efficiency of a Multi-Grid prototype of the T-REX spectrometer are presented for two different schemes of signal-processing electronics based on the VMM3A ASIC and CREMAT technology. These measurements, intended to test the suitability of VMM3A for readout of the T-REX Multi-Grid, are compared with Monte Carlo simulations based on the Garfield++ and Geant4 tool kits.
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All authors A.Backis, F.Piscitelli, D.Pfeiffer, J.R.M.Annand, M.Aouane, K.G.Fissum, K.Livingston, G.Mauri, D.Raspino
Plasma Physics
Accelerating kinetic plasma simulations with machine learning generated initial conditions
Andrew T. Powis, Domenica Corona Rivera, Alexander Khrabry et al. (+1 more)
Computer aided engineering of multi-time-scale
plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with traditional first-principles simulations and high-performance computing offer many interesting pathways towards resolving this challenge. We consider acceleration of kinetic
plasma simulations via machine learning generated initial conditions. The approach is demonstrated through modeling of capacitively coupled
plasma discharges relevant to the microelectronics industry. Three models are trained on simulations across a parameter space of device driving frequency and operating pressure. The models incorporate elements of a multi-layer perceptron, principal component analysis, and convolutional neural networks to predict the final time-averaged profiles of ion-density and velocity distribution functions. These data-driven initial condition generators (ICGs) provide a mean speedup of 17.1x in convergence time, when measured using an offline procedure, or a 4.4x speedup with an online procedure, with convolutional neural networks leading to the best performance. The paper also outlines a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins.
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All authors Andrew T. Powis, Domenica Corona Rivera, Alexander Khrabry, Igor D. Kaganovich
Space Physics · Cross-listed from astro-ph.EP
Impact Plasma Amplification of the Ancient Mercury Magnetic Field
Isaac S. Narrett, Rona Oran, Yuxi Chen et al. (+4 more)
Spacecraft measurements of Mercury indicate it has a core dynamo with a surface field of 200-800 nT. These data also indicate that the crust contains remanent magnetization likely produced by an ancient
magnetic field. The inferred magnetization intensity is consistent with a wide range of paleofield strengths (0.2-50 uT), possibly indicating that Mercury once had a dynamo field much stronger than today. Recent modeling of ancient lunar impacts has demonstrated that
plasma generated during basin-formation can transiently amplify a planetary dynamo field near the surface. Simultaneous impact-induced pressure waves can then record these fields in the form of crustal shock remanent magnetization (SRM). Here, we present impact hydrocode and magnetohydrodynamic simulations of a Caloris-size basin (~1,550 km diameter) formation event. Our results demonstrate that the ancient magnetospheric field (~0.5-0.9 uT) created by the interaction of the ancient interplanetary
magnetic field (IMF) and Mercury's dynamo field can be amplified by the
plasma up to ~13 uT and, via impact pressure waves, be recorded as SRM in the basin antipode. Such magnetization could produce ~5 nT crustal fields at 20-km altitude antipodal to Caloris detectable by future spacecraft like BepiColombo. Furthermore, impacts in the southern hemisphere that formed ~1,000 km diameter basins (e.g., Andal-Coleridge, Matisse-Repin, Eitkou-Milton, and Sadi-Scopus) could impart crustal magnetization in the northern hemisphere, contributing to the overall remanent field measured by MESSENGER. Overall, the impact
plasma amplification process can contribute to crustal magnetization on airless bodies and should be considered when reconstructing dynamo history from crustal anomaly measurements.
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All authors Isaac S. Narrett, Rona Oran, Yuxi Chen, Katarina Miljković, Gábor Tóth, Catherine L. Johnson, Benjamin P. Weiss
Optics
Parametric dependence of unidirectional guided resonances in periodic structures
Lijun Yuan and Ya Yan Lu
Unidirectional guided resonances (UGRs) in periodic structures are special resonant modes that exhibit strict one-sided radiation, even though radiation in both sides is allowed, offering significant advantages for various applications. Under a structural perturbation, a UGR typically turns to a regular resonant mode that radiates to both sides. Existing numerical results indicate that to find UGRs in any periodic structure, it is necessary to tune at least one parameter. In this work, we develop a rigorous theory on the parametric dependence of UGRs. We show that in the presence of a single radiation channel, a UGR can exist continuously with respect to a structural parameter, provided that another parameter (associated with a generic perturbation) is properly tuned. Moreover, from a periodic structure with a generic bound state in the continuum (BIC), it is always possible to obtain a continuous family of UGRs by tuning one parameter. This implies that UGRs with arbitrarily large quality factor can be easily obtained. Our work provides a theoretical basis for designing useful photonic devices based on UGRs.
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Plasma Physics · Cross-listed from math.DS
Numerical tests of formulae for volume enclosed by flux surfaces of integrable magnetic fields
David Martinez-del-Rio and Robert S. MacKay
Numerical tests of volume formulae are presented to compute efficiently the volume enclosed between flux surfaces for integrable 3D vector fields with various degrees of
symmetry. In the process, a new case is proposed and tested.
Data Analysis and Statistics · Cross-listed from eess.IV
GFSR-Net: Guided Focus via Segment-Wise Relevance Network for Interpretable Deep Learning in Medical Imaging
Jhonatan Contreras and Thomas Bocklitz
Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make correct predictions without explaining their
reasoning. They may also rely on image regions unrelated to the disease or visual cues, such as annotations, that are not present in real-world conditions. This can reduce trust and increase the risk of misleading diagnoses. We introduce the Guided Focus via Segment-Wise Relevance Network (GFSR-Net), an approach designed to improve interpretability and reliability in medical imaging. GFSR-Net uses a small number of human annotations to approximate where a person would focus within an image intuitively, without requiring precise boundaries or exhaustive markings, making the process fast and practical. During training, the model learns to align its focus with these areas, progressively emphasizing features that carry diagnostic meaning. This guidance works across different types of natural and medical images, including chest X-rays, retinal scans, and dermatological images. Our experiments demonstrate that GFSR achieves comparable or superior accuracy while producing saliency maps that better reflect human expectations. This reduces the reliance on irrelevant patterns and increases confidence in automated diagnostic tools.
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Computational Physics · Cross-listed from quant-ph
Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Adrián Mar{\i}n, Mauricio Soto-Gomez, Giorgio Valentini et al. (+3 more)
Graph Representation Learning (GRL) has emerged as a cornerstone technique for analysing complex, networked data across diverse domains, including biological systems, social networks, and data analysis. Traditional GRL methods often struggle to capture intricate relationships within complex graphs, particularly those exhibiting non-trivial structural properties such as power-law distributions or hierarchical structures. This paper introduces a novel
quantum-inspired
algorithm for GRL, utilizing hybrid
Quantum-Classical Walks to overcome these limitations. Our approach combines the benefits of both
quantum and classical dynamics, allowing the walker to simultaneously explore both highly local and far-reaching connections within the graph. Preliminary results for a case study in network community detection shows that this hybrid dynamic enables the
algorithm to adapt effectively to complex graph
topologies, offering a robust and versatile solution for GRL tasks.
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All authors Adrián Mar{\i}n, Mauricio Soto-Gomez, Giorgio Valentini, Elena Casiraghi, Carlos Cano, Daniel Manzano
Instrumentation and Detectors
Dark characterization of Ti/Al LEKIDs for the search of axions in the W-band
Victor Rollano, Alejandro Pascual Laguna, David Rodriguez et al. (+5 more)
We report the electrical (dark) characterization of lumped-element kinetic inductance detectors (LEKIDs) fabricated from a Titanium/Aluminum
bilayer and designed for broadband absorption in the W-band (75-110 GHz). These detectors are prototypes for future QCD
axion search experiments within the Canfranc
Axion Detection Experiment (CADEx), which demand sub 1e-19 W/Hz^0.5 sensitivities under low optical backgrounds. We combine a Mattis-Bardeen analysis to the temperature dependence of the detector parameters with noise spectroscopy to determine the electrical noise equivalent power (NEP). The minimum measured value for the electrical NEP is 3e-19 W/Hz0.5. Across the measured temperature range, we find that quasiparticle lifetime deviates from the expected BCS recombination law. Our analysis suggests that non-
equilibrium relaxation is governed by spatial inhomogeneities in the superconducting gap and
phonon diffusion effects. This work sets the road-map to achieve suitable and ultra-sensitive detectors in the W-band for dark matter
axion search experiments.
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All authors Victor Rollano, Alejandro Pascual Laguna, David Rodriguez, Martino Calvo, Maria Teresa Magaz, Daniel Granados, Alessandro Monfardini, Alicia Gomez